




'''simple docstring'''


import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey


def     SCREAMING_SNAKE_CASE							(   a_						:				int					,       a_						:				int					,       a_						:				int					,       a_						:				int					,       a_						:				int					,       a_						:				int      ):

  # prepare kernel
  # the kernel size have to be odd
  if (ksize % 2) == 0:
    __a								=       ksize + 1
  __a								=       np.zeros((ksize, ksize)					,       dtype=np.floataa      )

  # each value
  for y in range(a_      ):
    for x in range(a_      ):
      # distance from center
      __a								=       x - ksize // 2
      __a								=       y - ksize // 2

      # degree to radiant
      __a								=       theta / 180 * np.pi
      __a								=       np.cos(_theta      )
      __a								=       np.sin(_theta      )

      # get kernel x
      __a								=       cos_theta * px + sin_theta * py

      # get kernel y
      __a								=       -sin_theta * px + cos_theta * py

      # fill kernel
      __a								=       np.exp(
          -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)      ) * np.cos(2 * np.pi * _x / lambd + psi      )

  return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  UpperCAmelCase_					=   imread("../image_data/lena.jpg")
  # turn image in gray scale value
  UpperCAmelCase_					=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  UpperCAmelCase_					=   np.zeros(gray.shape[:2])
  for theta in [0, 30, 60, 90, 1_20, 1_50]:
    UpperCAmelCase_					=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  UpperCAmelCase_					=   out / out.max() * 2_55
  UpperCAmelCase_					=   out.astype(np.uinta)

  imshow("Original", gray)
  imshow("Gabor filter with 20x20 mask and 6 directions", out)

  waitKey(0)









'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



